The process of information retrieval involves two parties. On one hand there are producers of the information to be stored by the information retrieval system. The producers either actively publish the information to the system or let the information system select the information from the producer's source system (e.g. internet search engines work that way). On the other hand there are consumers of the information stored in the information retrieval system. Examples of published and stored information types, include, but are not limited to, social media messages, digitized documents (e.g. scanned and converted with optical character recognition), electronic message documents (e-mails), images, HTML pages, binary documents such as office documents, and text documents.
Information retrieval systems work by extracting information out of a source document and storing the corresponding document representation for later retrieval. The representation of a document is composed in parts of meta-data associated with the source document (data describing the source document) and data from the actual document content. Examples of meta data attributes, include, but are not limited to, document author name, document author social media platform identifier (e.g. twitter handle), document creation date, document modification date, length of document in number of bytes, length of document in number of characters, length of document in number of words, document domain (e.g. dietary publication in the field of medicine), and document content attributes (e.g. age group studied in the dietary publication).
Consumers of the system formulate queries that are formal statements of information needed. Queries are formulated by using arbitrary text keywords and, or, specific parts from the document meta-data. Examples of query terms, include, but are not limited to, individual text keywords, text phrases, meta-data attribute filters, and meta-data attribute range filters. The information retrieval system evaluates each query and locates matching document representations, with varying levels of relevance. The corresponding documents are returned to the user. The information retrieval system may also calculate the level of relevancy of each document representation and sort the returned documents according to the respective relevancy level.
One reason for the broad adaption of information retrieval systems is the large amount of digital information available. Technologies such as the public Internet, electronic messaging systems (e-mail), social media networks, and mobile devices allow producers to publish information more easily and thus more frequently. All resulting in an exponential digital information growth. On the consumer side information overload is a wide spread problem. There is too much information to be processed, thus consumers need tools to efficiently navigate through the information available and find the sub-set of information that satisfies their information need.
However, a typical information retrieval system may overwhelm the user with the result set returned for any given query. The result set may include a large number of results, making it difficult and time consuming for the user to comprehend the relevant results. The user may limit the number of search results by narrowing down the search query. However, the narrowed search query may not yield some results, which would otherwise be relevant.
Attempts have been made to improve the relevancy of the results of a search query based on relative expertise between a searcher (consumer) and the creator(s) and/or contributor(s) (producers) of a document. Personalized rankings of search results have also been provided. However, neither of these techniques has been proven to be very efficient in reducing the size of the result set and increasing the probability of relevant results, without narrowing down the search query.
There is a need for an information retrieval system that enables a user to limit the number of search results without narrowing down the search query.